The present invention relates to the targeting of sales announcements, promotions, advertisements, coupons and the like to customers, and delivery of such targeted announcements, etc. to the customers in print or in electronic form, for example by cell phones, email, ATM device, or by any other device capable of printing, displaying or otherwise presenting a commercial message.
Retailers, wholesalers, marketers, and manufacturers often distribute promotional offers, such as coupons, offering discounts and other incentives in order to reward valuable customers, attract new customers, or promote the sale of specific products or services identified in the promotional offers. (Both products and services may be the subject of promotional offers. For ease of discussion both are referred to herein simply as “products.”) Conventional promotional methods have a number of disadvantages. The creation, distribution, and handling of promotional offers is generally at a considerable cost and can require considerable infrastructure, particularly where the offer is communicated through printed material such as fliers, inserts or paper coupons. A typical newspaper insert or bulk mailing by a mass merchandiser for example may involve hundreds of thousands of pieces of paper that are distributed throughout a geographical area and that may require purchasers to tear off and hand in a coupon that must then be processed by the merchant. In addition, perhaps a more significant and far-reaching economic cost may arise from deterioration in customer relations as customers react more and more strenuously against the plethora of promotional offers bombarding them from email, direct mail, newspapers, and the Internet, to mention only a few of many possible channels.
Whatever the underlying motivation for any given promotional offer, the objective is the same—to induce the recipient to purchase the offered product. Each offer includes a discount or other incentive to encourage the recipient to accept the offer and purchase the subject product. The offer promoter realizes none of its anticipated benefits unless the offer is accepted, that is to say, unless the recipient purchases the promoted product. Motivated in part by the considerable cost and potential annoyance factor of large-scale conventional promotional campaigns, a need has been recognized for increasing the percentage of customers accepting each offer while decreasing the number of ineffective offers distributed to customers. This need has been partially addressed by selectively targeting customers for attention according to their history of past purchases or other relevant data. However, with the ever-increasing annoyance to the customer posed by increased numbers of unwanted offers, more precise and effective targeting is still needed. With the increased tendency of customers to ignore promotional offers altogether, or even to terminate relationships with promoters who persist in that annoyance, past targeting methods are no longer adequate and can even be detrimental. For example, many retail and online merchants have customer loyalty programs offering special promotions to repeat customers who have a loyalty card or have otherwise registered with the merchant. The basic motivation for the loyalty program is generally to further relations with the best customers by rewarding them with special promotional offers. But over-promotion can have the opposite effect of angering loyal customers who are annoyed at a barrage of unwanted promotions.
Another problem of conventional promotional methods is that they do not lend themselves to use on popular electronic terminals that are becoming a common form of customer interaction. New electronic terminal devices can have such limited capabilities that the distribution of general promotional offers is not practicable, and even limited distributions circumscribed by known targeting methods can be impractical or ineffective with many forms of electronic communication. For example, bank customers are sometimes confronted by promotional offers or advertisements when using the bank's ATM machines. Very few promotional offers can be presented in the brief few seconds that a customer typically spends at the ATM machine. Customers often avoid or even resent reading those very few offers if the offers do not consistently prove to be of personal interest to them as individuals. Cell phones impose even more severe constraints than ATM machines. Many customers consider their cell phones to be personal and consider commercial messages on their cell phones to be rude intrusions on their privacy. In addition, cell phones have a very limited screen for viewing promotional offers and call for an inconvenient sequence of keystrokes to manipulate promotions on the screen. Thus, for both the physical and relational reasons cell phones provide very little opportunity for successful promotional presentation with known technology.
In the past merchandisers have attempted to address the problem of individualized promotions by a process of targeting, generally meaning a technologically implemented method of matching promotional offers to one or more individual characteristics of customers. Targeting is currently carried out in a variety of ways for varying objectives and with varying success. Statistical methods can be applied to help identify the purchasing histories of those customers who would be most likely to purchase the product offered by any given promotion. Each customer's past purchasing history might be used to indicate the likelihood of purchasing any promoted product in the future. For example, a diaper promotion might be distributed to customers whose purchasing history reveals past purchases of baby bottles and baby food because those purchases imply a baby in the family and therefore a likelihood that baby diapers might be purchased in the future. This form of targeting is intended to identify those who are most likely to buy. In the reverse sense, targeting can exclude those who are least likely to buy. For example, a targeting process should not distribute a promotion for meat to vegetarians. The overall objective of targeting was, and still is, to significantly reduce the number of promotional offers distributed while significantly increasing the number accepted.
These forms of targeting might appear to be adequate but they are not. Several disadvantages arise. One is the disadvantage of inundating some customers with many promotional offers while depriving others of any. Wide disparities were to be expected because any random collection of offers is statistically likely to be favored much more by some customers than others and to be disfavored much more by some than others. In the past, statistical targeting has been product-based in the sense that each product being promoted was distributed to those customers with the greatest likelihood of accepting the promotional offer. To make product-based targeting work, some cutoff threshold of probability has to be specified to differentiate customers having a high probability of acceptance from those having a low probability of acceptance. The result is that some customers are likely to receive a disproportionately large number of offers while others receive very few or none. As a result of the disparity, many distributed offers or coupons are wasted, and some customers will be annoyed by a deluge of offers while others will be annoyed by the lack of attention. Targeted in that way, some customers could be expected to purchase only a small percentage of products offered because they receive many more offers than they could or would accept in a reasonable time period. Conversely, customers who receive very few offers will have very few to accept. Thus the various goals and purposes of targeting are contravened, and targeting does not effectively achieve the purposes for which it was intended.
A further disadvantage of the past targeting attempts is the inability to effectively control the number of promotional offers delivered to each individual customer while still retaining precision in targeting. Although past methods may be able to establish and enforce several different distribution limits, the manner in which those limits are maintained can also impose extremely severe disadvantages. For example, in the prior art of coupon distribution, there are sometimes limits on the number of coupons distributed in total, the number for each offer-communicating terminal, the number for each store, the number for each offer, and also the maximum number to be delivered to any one customer. The impositions of any or all such limits must result in the reduction of the number of coupons distributed to some customers. The selection of which coupons to withhold is typically based upon factors other than the purchase history of the customer, for example the age of the coupon or simply an arbitrary first-come-first-serve policy as the coupons are created. Thus some coupons that might have been distributed to a given customer because of that customer's purchasing statistics may be withheld because of some unrelated limit. The disadvantage arises in the fact that those coupons withheld from a customer because of limits might well have been the very coupons most likely to be redeemed by that customer. Therefore, the setting of limits in the past had the major disadvantage of distorting the targeting process. Some offers that were less likely to be redeemed by the customer might have been distributed while some that were more likely to be redeemed might not have.
A further disadvantage of the past targeting attempts is the statistical bias towards products that are more broadly used, rather than those more likely to be redeemed by each individual customer. The bias arises where the probability of purchasing a product in the future is estimated simply by the frequency of similar purchases in the past. For example, an offer of a 10% discount on bread might be distributed to almost all customers because almost all buy bread frequently. The statistical analysis is not normalized in the sense that it does not take into consideration the relative purchasing behavior between customers so that offers for bread might be distributed only to those who purchase bread much more often than others. Without normalization, customer purchasing statistics can misrepresent the intentions of the customer when confronted by a set of competing offers.
Similarly, the discount offered will generally affect the probability of acceptance. Therefore, statistical methods that do not consider the discount of the offer are not as precise as those that do. It is well accepted based on principles of supply and demand that the probability of a purchase increases with the size of the discount. That is to say, the sale of a $20 item is more probable when discounted to $10 than when discounted only to $15. For accurate targeting the merchandiser needs some way to appropriately increase the estimated probability of acceptance of an offer according to an increase in incentive value, whether the offer be a discount or a give-away after a prescribed number of units have been purchased.
Another disadvantage of past targeting attempts has been the lack of precision with sparse data. In cases where a great number of differing product items can be promoted, each individual customer is not likely to have purchased many of each item. Therefore, purchasing behavior data can lead to large variance in estimated means with the resulting imprecision in targeting.
Another disadvantage of past targeting attempts has been the inability of the merchandiser to vary the distribution according to a preprogrammed merchandising strategy. For example, one promotion might be offered to customers to entice them to switch from one brand to another, say from brand A to brand B. The targeted group consists of those customers of brand A. Some other promotion might be offered to entice them to purchase a more expensive and higher quality item than a similar item that they buy regularly. In that case, the targeted group consists of those customers who buy a less expensive equivalent to brand B. These two examples, among the multiplicity of different strategies, illustrate the need to adjust the class of targeted customers consistent with the goal of the selected strategy.
The growing customer resentment towards unwanted advertisements and unsolicited promotional offers is severely affecting the conduct of business. In email, unsolicited messages referred to as “spam” have given rise to many spam defeating products from major enterprises, such as “Spaminator” by Earthlink and “MSN-8 Junk Email Filter” by Microsoft Corporation. At least one major Internet service provider has initiated legal actions against five spam mailers as a result of complaints from 8 million customers. Telemarketing has become annoying enough to the general public that new laws have been enacted heavily penalizing a telemarketer who calls a telephone that is registered on a “Do Not Call” list. Unfortunately for the retailer, wholesaler, manufacturer, and customer, these attempts at curbing the intrusion of unwanted promotional offers tend to discourage promotional offers altogether without consideration of the individual differences between customers. In short, past marketing strategies have been so annoying and intrusive that they have engendered new products and new laws to block the marketing strategies altogether.